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                <div id="site-name"><a href="../../sphinx_pages/build/html/index.html">Shark machine learning library</a></div>
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<p><a href="inherits.html">Go to the graphical class hierarchy</a></p>
This inheritance list is sorted roughly, but not completely, alphabetically:</div><div class="directory">
<div class="levels">[detail level <span onclick="javascript:toggleLevel(1);">1</span><span onclick="javascript:toggleLevel(2);">2</span><span onclick="javascript:toggleLevel(3);">3</span><span onclick="javascript:toggleLevel(4);">4</span><span onclick="javascript:toggleLevel(5);">5</span><span onclick="javascript:toggleLevel(6);">6</span>]</div><table class="directory">
<tr id="row_0_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_0_" class="arrow" onclick="toggleFolder('0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_budget_maintenance_strategy.html" target="_self">shark::AbstractBudgetMaintenanceStrategy&lt; InputType &gt;</a></td><td class="desc">This is the abstract interface for any budget maintenance strategy </td></tr>
<tr id="row_0_0_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_merge_budget_maintenance_strategy.html" target="_self">shark::MergeBudgetMaintenanceStrategy&lt; InputType &gt;</a></td><td class="desc">Budget maintenance strategy that merges two vectors </td></tr>
<tr id="row_0_1_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_project_budget_maintenance_strategy.html" target="_self">shark::ProjectBudgetMaintenanceStrategy&lt; InputType &gt;</a></td><td class="desc">Budget maintenance strategy that projects a vector </td></tr>
<tr id="row_0_2_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_remove_budget_maintenance_strategy.html" target="_self">shark::RemoveBudgetMaintenanceStrategy&lt; InputType &gt;</a></td><td class="desc">Budget maintenance strategy that removes a vector </td></tr>
<tr id="row_1_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_1_" class="arrow" onclick="toggleFolder('1_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_budget_maintenance_strategy.html" target="_self">shark::AbstractBudgetMaintenanceStrategy&lt; RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_merge_budget_maintenance_strategy_3_01_real_vector_01_4.html" target="_self">shark::MergeBudgetMaintenanceStrategy&lt; RealVector &gt;</a></td><td class="desc">Budget maintenance strategy merging vectors </td></tr>
<tr id="row_1_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_project_budget_maintenance_strategy_3_01_real_vector_01_4.html" target="_self">shark::ProjectBudgetMaintenanceStrategy&lt; RealVector &gt;</a></td><td class="desc">Budget maintenance strategy that projects a vector </td></tr>
<tr id="row_2_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_2_" class="arrow" onclick="toggleFolder('2_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_constraint_handler.html" target="_self">shark::AbstractConstraintHandler&lt; SearchPointType &gt;</a></td><td class="desc">Implements the base class for constraint handling </td></tr>
<tr id="row_2_0_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_box_constraint_handler.html" target="_self">shark::BoxConstraintHandler&lt; SearchPointType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_3_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_3_" class="arrow" onclick="toggleFolder('3_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_constraint_handler.html" target="_self">shark::AbstractConstraintHandler&lt; Vector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_3_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_box_constraint_handler.html" target="_self">shark::BoxConstraintHandler&lt; Vector &gt;</a></td><td class="desc">Handler for box-constraints </td></tr>
<tr id="row_4_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_4_" class="arrow" onclick="toggleFolder('4_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_nearest_neighbors.html" target="_self">shark::AbstractNearestNeighbors&lt; InputType, LabelType &gt;</a></td><td class="desc">Interface for Nearest Neighbor queries </td></tr>
<tr id="row_4_0_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_simple_nearest_neighbors.html" target="_self">shark::SimpleNearestNeighbors&lt; InputType, LabelType &gt;</a></td><td class="desc">Brute force optimized nearest neighbor implementation </td></tr>
<tr id="row_4_1_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_tree_nearest_neighbors.html" target="_self">shark::TreeNearestNeighbors&lt; InputType, LabelType &gt;</a></td><td class="desc">Nearest Neighbors implementation using binary trees </td></tr>
<tr id="row_5_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_5_" class="arrow" onclick="toggleFolder('5_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_stopping_criterion.html" target="_self">shark::AbstractStoppingCriterion&lt; ResultSetT &gt;</a></td><td class="desc">Base class for stopping criteria of optimization algorithms </td></tr>
<tr id="row_5_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_generalization_loss.html" target="_self">shark::GeneralizationLoss&lt; PointType &gt;</a></td><td class="desc">The generalization loss calculates the relative increase of the validation error compared to the minimum training error </td></tr>
<tr id="row_5_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_generalization_quotient.html" target="_self">shark::GeneralizationQuotient&lt; PointType &gt;</a></td><td class="desc">SStopping criterion monitoring the quotient of generalization loss and training progress </td></tr>
<tr id="row_5_2_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_max_iterations.html" target="_self">shark::MaxIterations&lt; ResultSet &gt;</a></td><td class="desc">This stopping criterion stops after a fixed number of iterations </td></tr>
<tr id="row_5_3_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_training_error.html" target="_self">shark::TrainingError&lt; PointType &gt;</a></td><td class="desc">This stopping criterion tracks the improvement of the error function of the training error over an interval of iterations </td></tr>
<tr id="row_5_4_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_training_progress.html" target="_self">shark::TrainingProgress&lt; PointType &gt;</a></td><td class="desc">This stopping criterion tracks the improvement of the training error over an interval of iterations </td></tr>
<tr id="row_6_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_6_" class="arrow" onclick="toggleFolder('6_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_stopping_criterion.html" target="_self">shark::AbstractStoppingCriterion&lt; SingleObjectiveResultSet&lt; RealVector &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_6_0_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_validated_stopping_criterion.html" target="_self">shark::ValidatedStoppingCriterion</a></td><td class="desc">Given the current Result set of the optimizer, calculates the validation error using a validation function and hands the results over to the underlying stopping criterion </td></tr>
<tr id="row_7_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_stopping_criterion.html" target="_self">shark::AbstractStoppingCriterion&lt; ValidatedSingleObjectiveResultSet&lt; RealVector &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_8_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_additive_epsilon_indicator.html" target="_self">shark::AdditiveEpsilonIndicator</a></td><td class="desc">Implements the Additive approximation properties of sets </td></tr>
<tr id="row_9_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_bars_and_stripes.html" target="_self">shark::BarsAndStripes</a></td><td class="desc">Generates the Bars-And-Stripes problem. In this problem, a 4x4 image has either rows or columns of the same value </td></tr>
<tr id="row_10_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_base_d_c_non_dominated_sort.html" target="_self">shark::BaseDCNonDominatedSort</a></td><td class="desc">Divide-and-conquer algorithm for non-dominated sorting </td></tr>
<tr id="row_11_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_11_" class="arrow" onclick="toggleFolder('11_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1statistics_1_1_base_statistics_object.html" target="_self">shark::statistics::BaseStatisticsObject</a></td><td class="desc">Base class for all Statistic Objects to be used with <a class="el" href="structshark_1_1statistics_1_1_statistics.html" title="Generates Statistics over the results of an experiment.">Statistics</a> <br  />
 </td></tr>
<tr id="row_11_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1statistics_1_1_fraction_missing.html" target="_self">shark::statistics::FractionMissing</a></td><td class="desc">For a vector of points computes for every dimension the fraction of missing values </td></tr>
<tr id="row_11_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1statistics_1_1_mean.html" target="_self">shark::statistics::Mean</a></td><td class="desc">For a vector of points computes for every dimension the mean </td></tr>
<tr id="row_11_2_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_11_2_" class="arrow" onclick="toggleFolder('11_2_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1statistics_1_1_quantile.html" target="_self">shark::statistics::Quantile</a></td><td class="desc">For a vector of points computes for every dimension the p-quantile </td></tr>
<tr id="row_11_2_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1statistics_1_1_lower_quantile.html" target="_self">shark::statistics::LowerQuantile</a></td><td class="desc">For a vector of points computes for every dimension the 25%-quantile </td></tr>
<tr id="row_11_2_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1statistics_1_1_median.html" target="_self">shark::statistics::Median</a></td><td class="desc">For a vector of points computes for every dimension the median </td></tr>
<tr id="row_11_2_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1statistics_1_1_upper_quantile.html" target="_self">shark::statistics::UpperQuantile</a></td><td class="desc">For a vector of points computes for every dimension the 75%-quantile </td></tr>
<tr id="row_11_3_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1statistics_1_1_variance.html" target="_self">shark::statistics::Variance</a></td><td class="desc">For a vector of points computes for every dimension the variance </td></tr>
<tr id="row_12_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_batch_3_01shark_1_1blas_1_1compressed__vector_3_01_t_01_4_01_4.html" target="_self">shark::Batch&lt; shark::blas::compressed_vector&lt; T &gt; &gt;</a></td><td class="desc">Specialization for ublas compressed vectors which are compressed matrices in batch mode! </td></tr>
<tr id="row_13_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_batch_traits.html" target="_self">shark::BatchTraits&lt; BatchType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_14_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_batch_traits_3_01blas_1_1compressed__matrix_3_01_t_01_4_01_4.html" target="_self">shark::BatchTraits&lt; blas::compressed_matrix&lt; T &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_15_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_batch_traits_3_01blas_1_1dense__matrix__adaptor_3_01_t_00_01blas_1_1row__major_00_01_tag_00_01_device_01_4_01_4.html" target="_self">shark::BatchTraits&lt; blas::dense_matrix_adaptor&lt; T, blas::row_major, Tag, Device &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_16_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_batch_traits_3_01blas_1_1matrix_3_01_t_00_01blas_1_1row__major_00_01_device_01_4_01_4.html" target="_self">shark::BatchTraits&lt; blas::matrix&lt; T, blas::row_major, Device &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_17_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_batch_traits_3_01_weighted_data_batch_3_01_data_type_00_01_weight_type_01_4_01_4.html" target="_self">shark::BatchTraits&lt; WeightedDataBatch&lt; DataType, WeightType &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_18_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_bias_solver.html" target="_self">shark::BiasSolver&lt; Matrix &gt;</a></td><td class="desc"></td></tr>
<tr id="row_19_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_bias_solver_simplex.html" target="_self">shark::BiasSolverSimplex&lt; Matrix &gt;</a></td><td class="desc"></td></tr>
<tr id="row_20_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_20_" class="arrow" onclick="toggleFolder('20_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_binary_tree.html" target="_self">shark::BinaryTree&lt; InputT &gt;</a></td><td class="desc">Super class of binary space-partitioning trees </td></tr>
<tr id="row_20_0_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_k_d_tree.html" target="_self">shark::KDTree&lt; InputT &gt;</a></td><td class="desc">KD-tree, a binary space-partitioning tree </td></tr>
<tr id="row_20_1_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_l_c_tree.html" target="_self">shark::LCTree&lt; VectorType, CuttingAccuracy &gt;</a></td><td class="desc">LC-tree, a binary space-partitioning tree </td></tr>
<tr id="row_21_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_21_" class="arrow" onclick="toggleFolder('21_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_binary_tree.html" target="_self">shark::BinaryTree&lt; Container::value_type &gt;</a></td><td class="desc"></td></tr>
<tr id="row_21_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_k_h_c_tree.html" target="_self">shark::KHCTree&lt; Container, CuttingAccuracy &gt;</a></td><td class="desc">KHC-tree, a binary space-partitioning tree </td></tr>
<tr id="row_22_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_binary_tree.html" target="_self">shark::BinaryTree&lt; RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_23_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_bitflip_mutator.html" target="_self">shark::BitflipMutator</a></td><td class="desc">Bitflip mutation operator </td></tr>
<tr id="row_24_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_block_matrix2x2.html" target="_self">shark::BlockMatrix2x2&lt; Matrix &gt;</a></td><td class="desc">SVM regression matrix </td></tr>
<tr id="row_25_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_box_constrained_problem.html" target="_self">shark::BoxConstrainedProblem&lt; SVMProblem &gt;</a></td><td class="desc">Quadratic program with box constraints </td></tr>
<tr id="row_26_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_26_" class="arrow" onclick="toggleFolder('26_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_box_constrained_problem.html" target="_self">shark::BoxConstrainedProblem&lt; Problem &gt;</a></td><td class="desc"></td></tr>
<tr id="row_26_0_" class="odd" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_26_0_" class="arrow" onclick="toggleFolder('26_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_box_based_shrinking_strategy.html" target="_self">shark::BoxBasedShrinkingStrategy&lt; BoxConstrainedProblem&lt; Problem &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_26_0_0_" class="odd" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_box_constrained_shrinking_problem.html" target="_self">shark::BoxConstrainedShrinkingProblem&lt; Problem &gt;</a></td><td class="desc"></td></tr>
<tr id="row_27_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_boxed_s_v_m_problem.html" target="_self">shark::BoxedSVMProblem&lt; MatrixT &gt;</a></td><td class="desc">Boxed problem for alpha in [lower,upper]^n and equality constraints </td></tr>
<tr id="row_28_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_cached_matrix.html" target="_self">shark::CachedMatrix&lt; Matrix &gt;</a></td><td class="desc">Efficient quadratic matrix cache </td></tr>
<tr id="row_29_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_can_be_called.html" target="_self">shark::CanBeCalled&lt; Functor, Argument &gt;</a></td><td class="desc">Detects whether Functor(Argument) can be called </td></tr>
<tr id="row_30_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_can_be_called_3_01_r_07_5_08_07_t_08_00_01_argument_01_4.html" target="_self">shark::CanBeCalled&lt; R(*)(T), Argument &gt;</a></td><td class="desc"></td></tr>
<tr id="row_31_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_can_be_called_3_01_r_07_t_08_00_01_argument_01_4.html" target="_self">shark::CanBeCalled&lt; R(T), Argument &gt;</a></td><td class="desc"></td></tr>
<tr id="row_32_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_c_m_a_chromosome.html" target="_self">shark::CMAChromosome</a></td><td class="desc">Models a CMAChromosomeof the elitist (MO-)CMA-ES that encodes strategy parameters </td></tr>
<tr id="row_33_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_const_proxy_reference.html" target="_self">shark::ConstProxyReference&lt; T &gt;</a></td><td class="desc">Sets the type of ProxxyReference </td></tr>
<tr id="row_34_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_cross_entropy.html" target="_self">shark::CrossEntropy&lt; LabelType, OutputType &gt;</a></td><td class="desc">Error measure for classification tasks that can be used as the objective function for training </td></tr>
<tr id="row_35_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_crowding_distance.html" target="_self">shark::CrowdingDistance</a></td><td class="desc">Implements the Crowding Distance of a pareto front </td></tr>
<tr id="row_36_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_c_s_v_m_problem.html" target="_self">shark::CSVMProblem&lt; MatrixT &gt;</a></td><td class="desc">Problem formulation for binary C-SVM problems </td></tr>
<tr id="row_37_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_c_v_folds.html" target="_self">shark::CVFolds&lt; DatasetTypeT &gt;</a></td><td class="desc"></td></tr>
<tr id="row_38_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_c_v_folds.html" target="_self">shark::CVFolds&lt; DatasetType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_39_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_c_v_folds.html" target="_self">shark::CVFolds&lt; LabeledData&lt; RealVector, unsigned int &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_40_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_data_distribution.html" target="_self">shark::DataDistribution&lt; InputType &gt;</a></td><td class="desc">A <a class="el" href="classshark_1_1_data_distribution.html" title="A DataDistribution defines an unsupervised learning problem.">DataDistribution</a> defines an unsupervised learning problem </td></tr>
<tr id="row_41_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_41_" class="arrow" onclick="toggleFolder('41_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_data_distribution.html" target="_self">shark::DataDistribution&lt; RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_41_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_image_patches.html" target="_self">shark::ImagePatches</a></td><td class="desc">Given a set of images, draws a set of image patches of a given size </td></tr>
<tr id="row_41_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_normal_distributed_points.html" target="_self">shark::NormalDistributedPoints</a></td><td class="desc">Generates a set of normally distributed points </td></tr>
<tr id="row_42_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_data_view.html" target="_self">shark::DataView&lt; DatasetType &gt;</a></td><td class="desc">Constant time Element-Lookup for Datasets </td></tr>
<tr id="row_43_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_data_view.html" target="_self">shark::DataView&lt; const shark::LabeledData &gt;</a></td><td class="desc"></td></tr>
<tr id="row_44_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_data_view.html" target="_self">shark::DataView&lt; shark::Data&lt; InputType &gt; const &gt;</a></td><td class="desc"></td></tr>
<tr id="row_45_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_data_view.html" target="_self">shark::DataView&lt; shark::Data&lt; LabelType &gt; const &gt;</a></td><td class="desc"></td></tr>
<tr id="row_46_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_data_view.html" target="_self">shark::DataView&lt; shark::LabeledData const &gt;</a></td><td class="desc"></td></tr>
<tr id="row_47_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_difference_kernel_matrix.html" target="_self">shark::DifferenceKernelMatrix&lt; InputType, CacheType &gt;</a></td><td class="desc">SVM ranking matrix </td></tr>
<tr id="row_48_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1tags_1_1_discrete_space.html" target="_self">shark::tags::DiscreteSpace</a></td><td class="desc">A Tag for EnumerationSpaces. It tells the Functions, that the space is discrete and can be enumerated </td></tr>
<tr id="row_49_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_distant_modes.html" target="_self">shark::DistantModes</a></td><td class="desc">Creates a set of pattern (each later representing a mode) which than are randomly perturbed to create the data set. The dataset was introduced in Desjardins et al. (2010) (Parallel Tempering for training restricted Boltzmann machines, AISTATS 2010) </td></tr>
<tr id="row_50_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_double_pole.html" target="_self">shark::DoublePole</a></td><td class="desc"></td></tr>
<tr id="row_51_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_elitist_selection.html" target="_self">shark::ElitistSelection&lt; Ordering &gt;</a></td><td class="desc">Survival selection to find the next parent set </td></tr>
<tr id="row_52_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_energy.html" target="_self">shark::Energy&lt; RBM &gt;</a></td><td class="desc">The <a class="el" href="structshark_1_1_energy.html" title="The Energy function determining the Gibbs distribution of an RBM.">Energy</a> function determining the Gibbs distribution of an <a class="el" href="classshark_1_1_r_b_m.html" title="stub for the RBM class. at the moment it is just a holder of the parameter set and the Energy.">RBM</a> </td></tr>
<tr id="row_53_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_energy_storing_tempered_markov_chain.html" target="_self">shark::EnergyStoringTemperedMarkovChain&lt; Operator &gt;</a></td><td class="desc">Implements parallel tempering but also stores additional statistics on the energy differences </td></tr>
<tr id="row_54_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_qp_sparse_array_1_1_entry.html" target="_self">shark::QpSparseArray&lt; QpFloatType &gt;::Entry</a></td><td class="desc">Non-default (non-zero) array entry </td></tr>
<tr id="row_55_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_e_p_tournament_selection.html" target="_self">shark::EPTournamentSelection&lt; Ordering &gt;</a></td><td class="desc">Survival and mating selection to find the next parent set </td></tr>
<tr id="row_56_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_qp_mc_box_decomp_1_1_example.html" target="_self">shark::QpMcBoxDecomp&lt; Matrix &gt;::Example</a></td><td class="desc"><a class="el" href="classshark_1_1_data.html" title="Data container.">Data</a> structure describing one training example </td></tr>
<tr id="row_57_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_qp_mc_simplex_decomp_1_1_example.html" target="_self">shark::QpMcSimplexDecomp&lt; Matrix &gt;::Example</a></td><td class="desc"><a class="el" href="classshark_1_1_data.html" title="Data container.">Data</a> structure describing one training example </td></tr>
<tr id="row_58_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_example_modified_kernel_matrix.html" target="_self">shark::ExampleModifiedKernelMatrix&lt; InputType, CacheType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_59_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_59_" class="arrow" onclick="toggleFolder('59_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><b>std::exception</b></td><td class="desc">STL class </td></tr>
<tr id="row_59_0_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_59_0_" class="arrow" onclick="toggleFolder('59_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_exception.html" target="_self">shark::Exception</a></td><td class="desc">Top-level exception class of the shark library </td></tr>
<tr id="row_59_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_typed_feature_not_available_exception.html" target="_self">shark::TypedFeatureNotAvailableException&lt; Feature &gt;</a></td><td class="desc"><a class="el" href="classshark_1_1_exception.html" title="Top-level exception class of the shark library.">Exception</a> indicating the attempt to use a feature which is not supported </td></tr>
<tr id="row_60_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_fast_sigmoid_neuron.html" target="_self">shark::FastSigmoidNeuron</a></td><td class="desc">Fast sigmoidal function, which does not need to compute an exponential function </td></tr>
<tr id="row_61_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_individual_1_1_fitness_ordering.html" target="_self">shark::Individual&lt; PointType, FitnessTypeT, Chromosome &gt;::FitnessOrdering</a></td><td class="desc">Ordering relation by the fitness of the individuals(only single objective) </td></tr>
<tr id="row_62_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_gaussian_kernel_matrix.html" target="_self">shark::GaussianKernelMatrix&lt; T, CacheType &gt;</a></td><td class="desc">Efficient special case if the kernel is Gaussian and the inputs are sparse vectors </td></tr>
<tr id="row_63_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_general_quadratic_problem.html" target="_self">shark::GeneralQuadraticProblem&lt; MatrixT &gt;</a></td><td class="desc">Quadratic Problem with only Box-Constraints Let K the kernel matrix, than the problem has the form </td></tr>
<tr id="row_64_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_gibbs_operator.html" target="_self">shark::GibbsOperator&lt; RBMType &gt;</a></td><td class="desc">Implements Block Gibbs Sampling related transition operators for various temperatures </td></tr>
<tr id="row_65_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_h_m_g_selection_criterion.html" target="_self">shark::HMGSelectionCriterion</a></td><td class="desc"></td></tr>
<tr id="row_66_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_hypervolume_approximator.html" target="_self">shark::HypervolumeApproximator</a></td><td class="desc">Implements an FPRAS for approximating the volume of a set of high-dimensional objects. The algorithm is described in </td></tr>
<tr id="row_67_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_hypervolume_calculator.html" target="_self">shark::HypervolumeCalculator</a></td><td class="desc">Frontend for hypervolume calculation algorithms in m dimensions </td></tr>
<tr id="row_68_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_hypervolume_calculator2_d.html" target="_self">shark::HypervolumeCalculator2D</a></td><td class="desc">Implementation of the exact hypervolume calculation in 2 dimensions </td></tr>
<tr id="row_69_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_hypervolume_calculator3_d.html" target="_self">shark::HypervolumeCalculator3D</a></td><td class="desc">Implementation of the exact hypervolume calculation in 3 dimensions </td></tr>
<tr id="row_70_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_hypervolume_calculator_m_d_h_o_y.html" target="_self">shark::HypervolumeCalculatorMDHOY</a></td><td class="desc">Implementation of the exact hypervolume calculation in m dimensions </td></tr>
<tr id="row_71_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_hypervolume_calculator_m_d_w_f_g.html" target="_self">shark::HypervolumeCalculatorMDWFG</a></td><td class="desc">Implementation of the exact hypervolume calculation in m dimensions </td></tr>
<tr id="row_72_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_hypervolume_contribution.html" target="_self">shark::HypervolumeContribution</a></td><td class="desc">Frontend for hypervolume contribution algorithms in m dimensions </td></tr>
<tr id="row_73_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_hypervolume_contribution2_d.html" target="_self">shark::HypervolumeContribution2D</a></td><td class="desc">Finds the smallest/largest Contributors given 2D points </td></tr>
<tr id="row_74_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_hypervolume_contribution3_d.html" target="_self">shark::HypervolumeContribution3D</a></td><td class="desc">Finds the hypervolume contribution for points in 3DD </td></tr>
<tr id="row_75_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_hypervolume_contribution_approximator.html" target="_self">shark::HypervolumeContributionApproximator</a></td><td class="desc">Approximately determines the point of a set contributing the least hypervolume </td></tr>
<tr id="row_76_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_hypervolume_contribution_m_d.html" target="_self">shark::HypervolumeContributionMD</a></td><td class="desc">Finds the hypervolume contribution for points in MD </td></tr>
<tr id="row_77_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_hypervolume_indicator.html" target="_self">shark::HypervolumeIndicator</a></td><td class="desc">Calculates the hypervolume covered by a front of non-dominated points </td></tr>
<tr id="row_78_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_hypervolume_subset_selection2_d.html" target="_self">shark::HypervolumeSubsetSelection2D</a></td><td class="desc">Implementation of the exact hypervolume subset selection algorithm in 2 dimensions </td></tr>
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<tr id="row_79_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_cost.html" target="_self">shark::AbstractCost&lt; RealVector, RealVector &gt;</a></td><td class="desc"></td></tr>
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<tr id="row_79_16_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_kernel_classifier.html" target="_self">shark::KernelClassifier&lt; InputType &gt;</a></td><td class="desc">Linear classifier in a kernel feature space </td></tr>
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<tr id="row_79_16_2_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_nearest_neighbor_model_3_01_input_type_00_01unsigned_01int_01_4.html" target="_self">shark::NearestNeighborModel&lt; InputType, unsigned int &gt;</a></td><td class="desc"></td></tr>
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<tr id="row_79_16_3_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_linear_classifier.html" target="_self">shark::LinearClassifier&lt; VectorType &gt;</a></td><td class="desc">Basic linear classifier </td></tr>
<tr id="row_79_17_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_79_17_" class="arrow" onclick="toggleFolder('79_17_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_model.html" target="_self">shark::AbstractModel&lt; InputT, OutputT &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_17_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_clustering_model.html" target="_self">shark::ClusteringModel&lt; InputT, OutputT &gt;</a></td><td class="desc">Abstract model with associated clustering object </td></tr>
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<tr id="row_79_18_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_concatenated_model.html" target="_self">shark::ConcatenatedModel&lt; VectorType &gt;</a></td><td class="desc"><a class="el" href="classshark_1_1_concatenated_model.html" title="ConcatenatedModel concatenates two models such that the output of the first model is input to the sec...">ConcatenatedModel</a> concatenates two models such that the output of the first model is input to the second </td></tr>
<tr id="row_79_19_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_model.html" target="_self">shark::AbstractModel&lt; RealVector, RealVector, RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_20_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_model.html" target="_self">shark::AbstractModel&lt; InputT, unsigned int &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_21_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_79_21_" class="arrow" onclick="toggleFolder('79_21_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_model.html" target="_self">shark::AbstractModel&lt; InputType, RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_21_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_21_0_" class="arrow" onclick="toggleFolder('79_21_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_kernel_expansion.html" target="_self">shark::KernelExpansion&lt; InputType &gt;</a></td><td class="desc">Linear model in a kernel feature space </td></tr>
<tr id="row_79_21_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_missing_features_kernel_expansion.html" target="_self">shark::MissingFeaturesKernelExpansion&lt; InputType &gt;</a></td><td class="desc">Kernel expansion with missing features support For a choice of kernel, see <a class="el" href="group__kernels.html">Kernels</a> </td></tr>
<tr id="row_79_21_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_21_1_" class="arrow" onclick="toggleFolder('79_21_1_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><b>shark::detail::BaseNearestNeighbor&lt; InputType, LabelType &gt;</b></td><td class="desc"></td></tr>
<tr id="row_79_21_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_nearest_neighbor_model.html" target="_self">shark::NearestNeighborModel&lt; InputType, LabelType &gt;</a></td><td class="desc">NearestNeighbor model for classification and regression </td></tr>
<tr id="row_79_22_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_model.html" target="_self">shark::AbstractModel&lt; InputType, unsigned int, RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_23_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_model.html" target="_self">shark::AbstractModel&lt; InputT, RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_24_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_model.html" target="_self">shark::AbstractModel&lt; std::remove_pointer&lt; BaseModelType &gt;::type::InputType, VectorType, std::remove_pointer&lt; BaseModelType &gt;::type::ParameterVectorType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_25_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_79_25_" class="arrow" onclick="toggleFolder('79_25_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_objective_function.html" target="_self">shark::AbstractObjectiveFunction&lt; SearchPointType, ResultT &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_25_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_combined_objective_function.html" target="_self">shark::CombinedObjectiveFunction&lt; SearchPointType, ResultT &gt;</a></td><td class="desc">Linear combination of objective functions </td></tr>
<tr id="row_79_26_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_79_26_" class="arrow" onclick="toggleFolder('79_26_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_objective_function.html" target="_self">shark::AbstractObjectiveFunction&lt; RealVector, double &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_26_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_contrastive_divergence.html" target="_self">shark::ContrastiveDivergence&lt; Operator &gt;</a></td><td class="desc">Implements k-step Contrastive Divergence described by Hinton et al. (2006) </td></tr>
<tr id="row_79_26_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_cross_validation_error.html" target="_self">shark::CrossValidationError&lt; ModelTypeT, LabelTypeT &gt;</a></td><td class="desc">Cross-validation error for selection of hyper-parameters </td></tr>
<tr id="row_79_26_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_exact_gradient.html" target="_self">shark::ExactGradient&lt; RBMType &gt;</a></td><td class="desc"></td></tr>
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<tr id="row_79_26_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_loo_error.html" target="_self">shark::LooError&lt; ModelTypeT, LabelType &gt;</a></td><td class="desc">Leave-one-out error objective function </td></tr>
<tr id="row_79_26_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_loo_error_c_svm.html" target="_self">shark::LooErrorCSvm&lt; InputType, CacheType &gt;</a></td><td class="desc">Leave-one-out error, specifically optimized for C-SVMs </td></tr>
<tr id="row_79_26_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_merge_budget_maintenance_strategy_3_01_real_vector_01_4_1_1_merging_problem_function.html" target="_self">shark::MergeBudgetMaintenanceStrategy&lt; RealVector &gt;::MergingProblemFunction</a></td><td class="desc"></td></tr>
<tr id="row_79_26_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_multi_chain_approximator.html" target="_self">shark::MultiChainApproximator&lt; MarkovChainType &gt;</a></td><td class="desc">Approximates the gradient by taking samples from an ensemble of Markov chains running in parallel </td></tr>
<tr id="row_79_26_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_negative_gaussian_process_evidence.html" target="_self">shark::NegativeGaussianProcessEvidence&lt; InputType, OutputType, LabelType &gt;</a></td><td class="desc">Evidence for model selection of a regularization network/Gaussian process </td></tr>
<tr id="row_79_26_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_negative_log_likelihood.html" target="_self">shark::NegativeLogLikelihood</a></td><td class="desc">Computes the negative log likelihood of a dataset under a model </td></tr>
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<tr id="row_79_49_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_hierarchical_clustering.html" target="_self">shark::HierarchicalClustering&lt; InputT &gt;</a></td><td class="desc">Clusters defined by a binary space partitioning tree </td></tr>
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<tr id="row_79_50_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_epsilon_hinge_loss.html" target="_self">shark::EpsilonHingeLoss</a></td><td class="desc">Hinge-loss for large margin regression </td></tr>
<tr id="row_79_50_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_huber_loss.html" target="_self">shark::HuberLoss</a></td><td class="desc">Huber-loss for for robust regression </td></tr>
<tr id="row_79_50_0_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_squared_epsilon_hinge_loss.html" target="_self">shark::SquaredEpsilonHingeLoss</a></td><td class="desc">Hinge-loss for large margin regression using th squared two-norm </td></tr>
<tr id="row_79_50_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_50_1_" class="arrow" onclick="toggleFolder('79_50_1_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_loss.html" target="_self">shark::AbstractLoss&lt; blas::vector&lt; T, Device &gt;, blas::vector&lt; T, Device &gt; &gt;</a></td><td class="desc"></td></tr>
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<tr id="row_79_50_2_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html" target="_self">shark::CrossEntropy&lt; unsigned int, OutputType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_50_2_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_squared_loss_3_01_output_type_00_01unsigned_01int_01_4.html" target="_self">shark::SquaredLoss&lt; OutputType, unsigned int &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_50_3_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_50_3_" class="arrow" onclick="toggleFolder('79_50_3_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_loss.html" target="_self">shark::AbstractLoss&lt; unsigned int, unsigned int &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_50_3_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_discrete_loss.html" target="_self">shark::DiscreteLoss</a></td><td class="desc">Flexible loss for classification </td></tr>
<tr id="row_79_50_4_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_50_4_" class="arrow" onclick="toggleFolder('79_50_4_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_loss.html" target="_self">shark::AbstractLoss&lt; unsigned int, RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_50_4_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_hinge_loss.html" target="_self">shark::HingeLoss</a></td><td class="desc">Hinge-loss for large margin classification </td></tr>
<tr id="row_79_50_4_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_squared_hinge_loss.html" target="_self">shark::SquaredHingeLoss</a></td><td class="desc">Squared Hinge-loss for large margin classification </td></tr>
<tr id="row_79_50_5_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_50_5_" class="arrow" onclick="toggleFolder('79_50_5_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_loss.html" target="_self">shark::AbstractLoss&lt; Sequence, Sequence &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_50_5_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_squared_loss_3_01_sequence_00_01_sequence_01_4.html" target="_self">shark::SquaredLoss&lt; Sequence, Sequence &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_50_6_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_50_6_" class="arrow" onclick="toggleFolder('79_50_6_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_loss.html" target="_self">shark::AbstractLoss&lt; unsigned int, blas::vector&lt; Float &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_50_6_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_zero_one_loss_3_01unsigned_01int_00_01blas_1_1vector_3_01_float_01_4_01_4.html" target="_self">shark::ZeroOneLoss&lt; unsigned int, blas::vector&lt; Float &gt; &gt;</a></td><td class="desc">0-1-loss for classification </td></tr>
<tr id="row_79_50_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_loss.html" target="_self">shark::AbstractLoss&lt; SearchPointType, SearchPointType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_50_8_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_50_8_" class="arrow" onclick="toggleFolder('79_50_8_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_loss.html" target="_self">shark::AbstractLoss&lt; LabelT, OutputT &gt;</a></td><td class="desc">Loss function interface </td></tr>
<tr id="row_79_50_8_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_absolute_loss.html" target="_self">shark::AbsoluteLoss&lt; VectorType &gt;</a></td><td class="desc">Absolute loss </td></tr>
<tr id="row_79_50_8_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_squared_loss.html" target="_self">shark::SquaredLoss&lt; OutputType, LabelType &gt;</a></td><td class="desc">Squared loss for regression and classification </td></tr>
<tr id="row_79_50_8_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_zero_one_loss.html" target="_self">shark::ZeroOneLoss&lt; LabelType, OutputType &gt;</a></td><td class="desc">0-1-loss for classification </td></tr>
<tr id="row_79_50_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_negative_a_u_c.html" target="_self">shark::NegativeAUC&lt; LabelType, OutputType &gt;</a></td><td class="desc">Negative area under the curve </td></tr>
<tr id="row_79_50_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_negative_wilcoxon_mann_whitney_statistic.html" target="_self">shark::NegativeWilcoxonMannWhitneyStatistic&lt; LabelType, OutputType &gt;</a></td><td class="desc">Negative Wilcoxon-Mann-Whitney statistic </td></tr>
<tr id="row_79_51_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_79_51_" class="arrow" onclick="toggleFolder('79_51_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_metric.html" target="_self">shark::AbstractMetric&lt; InputTypeT &gt;</a></td><td class="desc">Base-class for metrics </td></tr>
<tr id="row_79_51_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_kernel_function.html" target="_self">shark::AbstractKernelFunction&lt; RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_51_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_51_1_" class="arrow" onclick="toggleFolder('79_51_1_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_kernel_function.html" target="_self">shark::AbstractKernelFunction&lt; std::size_t &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_51_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_79_51_1_0_" class="arrow" onclick="toggleFolder('79_51_1_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_discrete_kernel.html" target="_self">shark::DiscreteKernel</a></td><td class="desc">Kernel on a finite, discrete space </td></tr>
<tr id="row_79_51_1_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_gaussian_task_kernel.html" target="_self">shark::GaussianTaskKernel&lt; InputTypeT &gt;</a></td><td class="desc">Special "Gaussian-like" kernel function on tasks </td></tr>
<tr id="row_79_51_2_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_51_2_" class="arrow" onclick="toggleFolder('79_51_2_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_kernel_function.html" target="_self">shark::AbstractKernelFunction&lt; InputType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_51_2_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_79_51_2_0_" class="arrow" onclick="toggleFolder('79_51_2_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_weighted_sum_kernel.html" target="_self">shark::WeightedSumKernel&lt; InputType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_51_2_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_mkl_kernel.html" target="_self">shark::MklKernel&lt; InputType &gt;</a></td><td class="desc">Weighted sum of kernel functions </td></tr>
<tr id="row_79_51_2_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_product_kernel.html" target="_self">shark::ProductKernel&lt; InputType &gt;</a></td><td class="desc">Product of kernel functions </td></tr>
<tr id="row_79_51_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_kernel_function.html" target="_self">shark::AbstractKernelFunction&lt; MultiTaskSample&lt; InputTypeT &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_51_4_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_51_4_" class="arrow" onclick="toggleFolder('79_51_4_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_kernel_function.html" target="_self">shark::AbstractKernelFunction&lt; InputTypeT &gt;</a></td><td class="desc">Base class of all Kernel functions </td></tr>
<tr id="row_79_51_4_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_79_51_4_0_" class="arrow" onclick="toggleFolder('79_51_4_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_product_kernel.html" target="_self">shark::ProductKernel&lt; MultiTaskSample&lt; InputTypeT &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_51_4_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_multi_task_kernel.html" target="_self">shark::MultiTaskKernel&lt; InputTypeT &gt;</a></td><td class="desc">Special kernel function for multi-task and transfer learning </td></tr>
<tr id="row_79_51_4_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_a_r_d_kernel_unconstrained.html" target="_self">shark::ARDKernelUnconstrained&lt; InputType &gt;</a></td><td class="desc">Automatic relevance detection kernel for unconstrained parameter optimization </td></tr>
<tr id="row_79_51_4_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_gaussian_rbf_kernel.html" target="_self">shark::GaussianRbfKernel&lt; InputType &gt;</a></td><td class="desc">Gaussian radial basis function kernel </td></tr>
<tr id="row_79_51_4_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_linear_kernel.html" target="_self">shark::LinearKernel&lt; InputType &gt;</a></td><td class="desc">Linear Kernel, parameter free </td></tr>
<tr id="row_79_51_4_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_model_kernel.html" target="_self">shark::ModelKernel&lt; InputType &gt;</a></td><td class="desc">Kernel function that uses a Model as transformation function for another kernel </td></tr>
<tr id="row_79_51_4_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_monomial_kernel.html" target="_self">shark::MonomialKernel&lt; InputType &gt;</a></td><td class="desc">Monomial kernel. Calculates \( \left\langle x_1, x_2 \right\rangle^m_exponent \) </td></tr>
<tr id="row_79_51_4_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_normalized_kernel.html" target="_self">shark::NormalizedKernel&lt; InputType &gt;</a></td><td class="desc">Normalized version of a kernel function </td></tr>
<tr id="row_79_51_4_7_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_point_set_kernel.html" target="_self">shark::PointSetKernel&lt; InputType &gt;</a></td><td class="desc">Normalized version of a kernel function </td></tr>
<tr id="row_79_51_4_8_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_polynomial_kernel.html" target="_self">shark::PolynomialKernel&lt; InputType &gt;</a></td><td class="desc">Polynomial kernel </td></tr>
<tr id="row_79_51_4_9_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_scaled_kernel.html" target="_self">shark::ScaledKernel&lt; InputType &gt;</a></td><td class="desc">Scaled version of a kernel function </td></tr>
<tr id="row_79_51_4_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_79_51_4_10_" class="arrow" onclick="toggleFolder('79_51_4_10_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_weighted_sum_kernel.html" target="_self">shark::WeightedSumKernel&lt; InputType &gt;</a></td><td class="desc">Weighted sum of kernel functions </td></tr>
<tr id="row_79_51_4_10_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_subrange_kernel.html" target="_self">shark::SubrangeKernel&lt; InputType, InnerKernel &gt;</a></td><td class="desc">Weighted sum of kernel functions </td></tr>
<tr id="row_79_52_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_79_52_" class="arrow" onclick="toggleFolder('79_52_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_model.html" target="_self">shark::AbstractModel&lt; InputTypeT, OutputTypeT, ParameterVectorType &gt;</a></td><td class="desc">Base class for all Models </td></tr>
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<tr id="row_79_52_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_hard_clustering_model.html" target="_self">shark::HardClusteringModel&lt; InputT &gt;</a></td><td class="desc">Model for "hard" clustering </td></tr>
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<tr id="row_79_53_31_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1benchmarks_1_1_multi_objective_benchmark.html" target="_self">shark::benchmarks::MultiObjectiveBenchmark&lt; Objectives &gt;</a></td><td class="desc">Creates a multi-objective Benchmark from a set of given single objective functions </td></tr>
<tr id="row_79_53_32_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1benchmarks_1_1_z_d_t1.html" target="_self">shark::benchmarks::ZDT1</a></td><td class="desc">Multi-objective optimization benchmark function <a class="el" href="structshark_1_1benchmarks_1_1_z_d_t1.html" title="Multi-objective optimization benchmark function ZDT1.">ZDT1</a> </td></tr>
<tr id="row_79_53_33_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1benchmarks_1_1_z_d_t2.html" target="_self">shark::benchmarks::ZDT2</a></td><td class="desc">Multi-objective optimization benchmark function <a class="el" href="structshark_1_1benchmarks_1_1_z_d_t2.html" title="Multi-objective optimization benchmark function ZDT2.">ZDT2</a> </td></tr>
<tr id="row_79_53_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1benchmarks_1_1_z_d_t3.html" target="_self">shark::benchmarks::ZDT3</a></td><td class="desc">Multi-objective optimization benchmark function <a class="el" href="structshark_1_1benchmarks_1_1_z_d_t3.html" title="Multi-objective optimization benchmark function ZDT3.">ZDT3</a> </td></tr>
<tr id="row_79_53_35_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1benchmarks_1_1_z_d_t4.html" target="_self">shark::benchmarks::ZDT4</a></td><td class="desc">Multi-objective optimization benchmark function <a class="el" href="structshark_1_1benchmarks_1_1_z_d_t4.html" title="Multi-objective optimization benchmark function ZDT4.">ZDT4</a> </td></tr>
<tr id="row_79_53_36_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1benchmarks_1_1_z_d_t6.html" target="_self">shark::benchmarks::ZDT6</a></td><td class="desc">Multi-objective optimization benchmark function <a class="el" href="structshark_1_1benchmarks_1_1_z_d_t6.html" title="Multi-objective optimization benchmark function ZDT6.">ZDT6</a> </td></tr>
<tr id="row_79_54_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_79_54_" class="arrow" onclick="toggleFolder('79_54_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_optimizer.html" target="_self">shark::AbstractOptimizer&lt; PointType, ResultT, SolutionTypeT &gt;</a></td><td class="desc">An optimizer that optimizes general objective functions </td></tr>
<tr id="row_79_54_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_54_0_" class="arrow" onclick="toggleFolder('79_54_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_multi_objective_optimizer.html" target="_self">shark::AbstractMultiObjectiveOptimizer&lt; RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_54_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_79_54_0_0_" class="arrow" onclick="toggleFolder('79_54_0_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_indicator_based_real_coded_n_s_g_a_i_i.html" target="_self">shark::IndicatorBasedRealCodedNSGAII&lt; NSGA3Indicator &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_54_0_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_real_coded_n_s_g_a_i_i_i.html" target="_self">shark::RealCodedNSGAIII</a></td><td class="desc">Implements the NSGA-III </td></tr>
<tr id="row_79_54_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_indicator_based_m_o_c_m_a.html" target="_self">shark::IndicatorBasedMOCMA&lt; Indicator &gt;</a></td><td class="desc">Implements the generational MO-CMA-ES </td></tr>
<tr id="row_79_54_0_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_indicator_based_real_coded_n_s_g_a_i_i.html" target="_self">shark::IndicatorBasedRealCodedNSGAII&lt; Indicator &gt;</a></td><td class="desc">Implements the NSGA-II </td></tr>
<tr id="row_79_54_0_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_indicator_based_steady_state_m_o_c_m_a.html" target="_self">shark::IndicatorBasedSteadyStateMOCMA&lt; Indicator &gt;</a></td><td class="desc">Implements the \((\mu+1)\)-MO-CMA-ES </td></tr>
<tr id="row_79_54_0_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_m_o_e_a_d.html" target="_self">shark::MOEAD</a></td><td class="desc">Implements the MOEA/D algorithm </td></tr>
<tr id="row_79_54_0_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_r_v_e_a.html" target="_self">shark::RVEA</a></td><td class="desc">Implements the <a class="el" href="classshark_1_1_r_v_e_a.html" title="Implements the RVEA algorithm.">RVEA</a> algorithm </td></tr>
<tr id="row_79_54_0_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_s_m_s_e_m_o_a.html" target="_self">shark::SMSEMOA</a></td><td class="desc">Implements the SMS-EMOA </td></tr>
<tr id="row_79_54_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_54_1_" class="arrow" onclick="toggleFolder('79_54_1_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_single_objective_optimizer.html" target="_self">shark::AbstractSingleObjectiveOptimizer&lt; SearchPointType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_54_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_79_54_1_0_" class="arrow" onclick="toggleFolder('79_54_1_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_line_search_optimizer.html" target="_self">shark::AbstractLineSearchOptimizer&lt; SearchPointType &gt;</a></td><td class="desc">Basis class for line search methods </td></tr>
<tr id="row_79_54_1_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_b_f_g_s.html" target="_self">shark::BFGS&lt; SearchPointType &gt;</a></td><td class="desc">Broyden, Fletcher, Goldfarb, Shannon algorithm for unconstraint optimization </td></tr>
<tr id="row_79_54_1_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_c_g.html" target="_self">shark::CG&lt; SearchPointType &gt;</a></td><td class="desc">Conjugate-gradient method for unconstrained optimization </td></tr>
<tr id="row_79_54_1_0_2_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_l_b_f_g_s.html" target="_self">shark::LBFGS&lt; SearchPointType &gt;</a></td><td class="desc">Limited-Memory Broyden, Fletcher, Goldfarb, Shannon algorithm </td></tr>
<tr id="row_79_54_2_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_54_2_" class="arrow" onclick="toggleFolder('79_54_2_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_single_objective_optimizer.html" target="_self">shark::AbstractSingleObjectiveOptimizer&lt; RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_54_2_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_c_m_a.html" target="_self">shark::CMA</a></td><td class="desc">Implements the CMA-ES </td></tr>
<tr id="row_79_54_2_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_c_m_s_a.html" target="_self">shark::CMSA</a></td><td class="desc">Implements the <a class="el" href="classshark_1_1_c_m_s_a.html" title="Implements the CMSA.">CMSA</a> </td></tr>
<tr id="row_79_54_2_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_cross_entropy_method.html" target="_self">shark::CrossEntropyMethod</a></td><td class="desc">Implements the Cross Entropy Method </td></tr>
<tr id="row_79_54_2_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_elitist_c_m_a.html" target="_self">shark::ElitistCMA</a></td><td class="desc">Implements the elitist CMA-ES </td></tr>
<tr id="row_79_54_2_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_grid_search.html" target="_self">shark::GridSearch</a></td><td class="desc">Optimize by trying out a grid of configurations </td></tr>
<tr id="row_79_54_2_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_l_m_c_m_a.html" target="_self">shark::LMCMA</a></td><td class="desc">Implements a Limited-Memory-CMA </td></tr>
<tr id="row_79_54_2_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_nested_grid_search.html" target="_self">shark::NestedGridSearch</a></td><td class="desc">Nested grid search </td></tr>
<tr id="row_79_54_2_7_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_point_search.html" target="_self">shark::PointSearch</a></td><td class="desc">Optimize by trying out predefined configurations </td></tr>
<tr id="row_79_54_2_8_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_simplex_downhill.html" target="_self">shark::SimplexDownhill</a></td><td class="desc">Simplex Downhill Method </td></tr>
<tr id="row_79_54_2_9_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_trust_region_newton.html" target="_self">shark::TrustRegionNewton</a></td><td class="desc">Simple Trust-Region method based on the full Hessian matrix </td></tr>
<tr id="row_79_54_2_10_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_v_d_c_m_a.html" target="_self">shark::VDCMA</a></td><td class="desc"></td></tr>
<tr id="row_79_55_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_79_55_" class="arrow" onclick="toggleFolder('79_55_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_trainer.html" target="_self">shark::AbstractTrainer&lt; Model, LabelTypeT &gt;</a></td><td class="desc">Superclass of supervised learning algorithms </td></tr>
<tr id="row_79_55_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_55_0_" class="arrow" onclick="toggleFolder('79_55_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_svm_trainer.html" target="_self">shark::AbstractSvmTrainer&lt; InputType, RealVector, KernelExpansion&lt; InputType &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_55_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_epsilon_svm_trainer.html" target="_self">shark::EpsilonSvmTrainer&lt; InputType, CacheType &gt;</a></td><td class="desc">Training of Epsilon-SVMs for regression </td></tr>
<tr id="row_79_55_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_regularization_network_trainer.html" target="_self">shark::RegularizationNetworkTrainer&lt; InputType &gt;</a></td><td class="desc">Training of a regularization network </td></tr>
<tr id="row_79_55_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_55_1_" class="arrow" onclick="toggleFolder('79_55_1_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_svm_trainer.html" target="_self">shark::AbstractSvmTrainer&lt; InputType, unsigned int, MissingFeaturesKernelExpansion&lt; InputType &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_55_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_missing_feature_svm_trainer.html" target="_self">shark::MissingFeatureSvmTrainer&lt; InputType, CacheType &gt;</a></td><td class="desc">Trainer for binary SVMs natively supporting missing features </td></tr>
<tr id="row_79_55_2_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_55_2_" class="arrow" onclick="toggleFolder('79_55_2_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_svm_trainer.html" target="_self">shark::AbstractSvmTrainer&lt; InputType, unsigned int, KernelExpansion&lt; InputType &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_55_2_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_ranking_svm_trainer.html" target="_self">shark::RankingSvmTrainer&lt; InputType, CacheType &gt;</a></td><td class="desc">Training of an SVM for ranking </td></tr>
<tr id="row_79_55_3_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_55_3_" class="arrow" onclick="toggleFolder('79_55_3_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_svm_trainer.html" target="_self">shark::AbstractSvmTrainer&lt; InputType, unsigned int &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_55_3_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_squared_hinge_c_svm_trainer.html" target="_self">shark::SquaredHingeCSvmTrainer&lt; InputType, CacheType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_55_4_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_55_4_" class="arrow" onclick="toggleFolder('79_55_4_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_weighted_trainer.html" target="_self">shark::AbstractWeightedTrainer&lt; KernelClassifier&lt; InputType &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_55_4_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_79_55_4_0_" class="arrow" onclick="toggleFolder('79_55_4_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_svm_trainer.html" target="_self">shark::AbstractSvmTrainer&lt; InputType, unsigned int, KernelClassifier&lt; InputType &gt;, AbstractWeightedTrainer&lt; KernelClassifier&lt; InputType &gt; &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_55_4_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_c_svm_trainer.html" target="_self">shark::CSvmTrainer&lt; InputType, CacheType &gt;</a></td><td class="desc">Training of C-SVMs for binary classification </td></tr>
<tr id="row_79_55_5_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_55_5_" class="arrow" onclick="toggleFolder('79_55_5_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_weighted_trainer.html" target="_self">shark::AbstractWeightedTrainer&lt; KernelClassifier&lt; InputType &gt;, unsigned int &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_55_5_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_kernel_mean_classifier.html" target="_self">shark::KernelMeanClassifier&lt; InputType &gt;</a></td><td class="desc">Kernelized mean-classifier </td></tr>
<tr id="row_79_55_6_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_55_6_" class="arrow" onclick="toggleFolder('79_55_6_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_weighted_trainer.html" target="_self">shark::AbstractWeightedTrainer&lt; LinearClassifier&lt;&gt;, unsigned int &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_55_6_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_l_d_a.html" target="_self">shark::LDA</a></td><td class="desc">Linear Discriminant Analysis (<a class="el" href="classshark_1_1_l_d_a.html" title="Linear Discriminant Analysis (LDA)">LDA</a>) </td></tr>
<tr id="row_79_55_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_weighted_trainer.html" target="_self">shark::AbstractWeightedTrainer&lt; LinearClassifier&lt; RealVector &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_55_8_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_55_8_" class="arrow" onclick="toggleFolder('79_55_8_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_weighted_trainer.html" target="_self">shark::AbstractWeightedTrainer&lt; RFClassifier&lt; RealVector &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_55_8_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html" target="_self">shark::RFTrainer&lt; RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_55_9_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_79_55_9_" class="arrow" onclick="toggleFolder('79_55_9_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_weighted_trainer.html" target="_self">shark::AbstractWeightedTrainer&lt; RFClassifier&lt; unsigned int &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_55_9_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html" target="_self">shark::RFTrainer&lt; unsigned int &gt;</a></td><td class="desc"></td></tr>
<tr id="row_79_55_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_svm_trainer.html" target="_self">shark::AbstractSvmTrainer&lt; InputType, LabelType, Model, Trainer &gt;</a></td><td class="desc">Super class of all kernelized (non-linear) SVM trainers </td></tr>
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<tr id="row_79_55_11_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_linear_s_a_g_trainer.html" target="_self">shark::LinearSAGTrainer&lt; InputType, LabelType &gt;</a></td><td class="desc">Stochastic Average Gradient Method for training of linear models, </td></tr>
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<tr id="row_79_56_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_79_56_" class="arrow" onclick="toggleFolder('79_56_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_unsupervised_trainer.html" target="_self">shark::AbstractUnsupervisedTrainer&lt; Model &gt;</a></td><td class="desc">Superclass of unsupervised learning algorithms </td></tr>
<tr id="row_79_56_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_weighted_unsupervised_trainer.html" target="_self">shark::AbstractWeightedUnsupervisedTrainer&lt; Model &gt;</a></td><td class="desc">Superclass of weighted unsupervised learning algorithms </td></tr>
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<tr id="row_79_56_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_normalize_components_unit_variance.html" target="_self">shark::NormalizeComponentsUnitVariance&lt; DataType &gt;</a></td><td class="desc">Train a linear model to normalize the components of a dataset to unit variance, and optionally to zero mean </td></tr>
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<tr id="row_80_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_indicator_based_selection.html" target="_self">shark::IndicatorBasedSelection&lt; Indicator &gt;</a></td><td class="desc">Implements the well-known indicator-based selection strategy </td></tr>
<tr id="row_81_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_indicator_based_selection.html" target="_self">shark::IndicatorBasedSelection&lt; HypervolumeIndicator &gt;</a></td><td class="desc"></td></tr>
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<tr id="row_84_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_84_" class="arrow" onclick="toggleFolder('84_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_individual.html" target="_self">shark::Individual&lt; PointType, FitnessTypeT, Chromosome &gt;</a></td><td class="desc"><a class="el" href="classshark_1_1_individual.html" title="Individual is a simple templated class modelling an individual that acts as a candidate solution in a...">Individual</a> is a simple templated class modelling an individual that acts as a candidate solution in an evolutionary algorithm </td></tr>
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<tr id="row_92_49_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_line_search.html" target="_self">shark::LineSearch&lt; RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_92_50_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_typed_flags.html" target="_self">shark::TypedFlags&lt; Feature &gt;</a></td><td class="desc"></td></tr>
<tr id="row_92_51_" class="odd" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_92_51_" class="arrow" onclick="toggleFolder('92_51_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><b>shark::detail::BaseWeightedDataset&lt; LabeledData&lt; InputT, LabelT &gt; &gt;</b></td><td class="desc"></td></tr>
<tr id="row_92_51_0_" class="odd" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_weighted_labeled_data.html" target="_self">shark::WeightedLabeledData&lt; InputT, LabelT &gt;</a></td><td class="desc">Weighted data set for supervised learning </td></tr>
<tr id="row_92_52_" class="odd" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_92_52_" class="arrow" onclick="toggleFolder('92_52_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><b>shark::detail::BaseWeightedDataset&lt; UnlabeledData&lt; DataT &gt; &gt;</b></td><td class="desc"></td></tr>
<tr id="row_92_52_0_" class="odd" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_weighted_unlabeled_data.html" target="_self">shark::WeightedUnlabeledData&lt; DataT &gt;</a></td><td class="desc">Weighted data set for unsupervised learning </td></tr>
<tr id="row_92_53_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_clustering.html" target="_self">shark::AbstractClustering&lt; InputT &gt;</a></td><td class="desc">Base class for clustering </td></tr>
<tr id="row_92_54_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_metric.html" target="_self">shark::AbstractMetric&lt; InputTypeT &gt;</a></td><td class="desc">Base-class for metrics </td></tr>
<tr id="row_92_55_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_model.html" target="_self">shark::AbstractModel&lt; InputTypeT, OutputTypeT, ParameterVectorType &gt;</a></td><td class="desc">Base class for all Models </td></tr>
<tr id="row_92_56_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_optimizer.html" target="_self">shark::AbstractOptimizer&lt; PointType, ResultT, SolutionTypeT &gt;</a></td><td class="desc">An optimizer that optimizes general objective functions </td></tr>
<tr id="row_92_57_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_trainer.html" target="_self">shark::AbstractTrainer&lt; Model, LabelTypeT &gt;</a></td><td class="desc">Superclass of supervised learning algorithms </td></tr>
<tr id="row_92_58_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_unsupervised_trainer.html" target="_self">shark::AbstractUnsupervisedTrainer&lt; Model &gt;</a></td><td class="desc">Superclass of unsupervised learning algorithms </td></tr>
<tr id="row_92_59_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_binary_layer.html" target="_self">shark::BinaryLayer</a></td><td class="desc">Layer of binary units taking values in {0,1} </td></tr>
<tr id="row_92_60_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_bipolar_layer.html" target="_self">shark::BipolarLayer</a></td><td class="desc">Layer of bipolar units taking values in {-1,1} </td></tr>
<tr id="row_92_61_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_c_svm_derivative.html" target="_self">shark::CSvmDerivative&lt; InputType, CacheType &gt;</a></td><td class="desc">This class provides two main member functions for computing the derivative of a C-SVM hypothesis w.r.t. its hyperparameters. The constructor takes a pointer to a <a class="el" href="structshark_1_1_kernel_classifier.html" title="Linear classifier in a kernel feature space.">KernelClassifier</a> and an SvmTrainer, in the assumption that the former was trained by the latter. It heavily accesses their members to calculate the derivative of the alpha and offset values w.r.t. the SVM hyperparameters, that is, the regularization parameter C and the kernel parameters. This is done in the member function prepareCSvmParameterDerivative called by the constructor. After this initial, heavier computation step, modelCSvmParameterDerivative can be called on an input sample to the SVM model, and the method will yield the derivative of the hypothesis w.r.t. the SVM hyperparameters </td></tr>
<tr id="row_92_62_" class="odd" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_92_62_" class="arrow" onclick="toggleFolder('92_62_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_data.html" target="_self">shark::Data&lt; Type &gt;</a></td><td class="desc"><a class="el" href="classshark_1_1_data.html" title="Data container.">Data</a> container </td></tr>
<tr id="row_92_62_0_" class="odd" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_unlabeled_data.html" target="_self">shark::UnlabeledData&lt; RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_92_62_1_" class="odd" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_unlabeled_data.html" target="_self">shark::UnlabeledData&lt; InputType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_92_62_2_" class="odd" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_unlabeled_data.html" target="_self">shark::UnlabeledData&lt; SearchPointType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_92_63_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_gaussian_layer.html" target="_self">shark::GaussianLayer</a></td><td class="desc">A layer of Gaussian neurons </td></tr>
<tr id="row_92_64_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_labeled_data.html" target="_self">shark::LabeledData&lt; InputT, LabelT &gt;</a></td><td class="desc"><a class="el" href="classshark_1_1_data.html" title="Data container.">Data</a> set for supervised learning </td></tr>
<tr id="row_92_65_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_line_search.html" target="_self">shark::LineSearch&lt; SearchPointType &gt;</a></td><td class="desc">Wrapper for the linesearch class of functions in the linear algebra library </td></tr>
<tr id="row_92_66_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_multi_task_sample.html" target="_self">shark::MultiTaskSample&lt; InputTypeT &gt;</a></td><td class="desc">Aggregation of input data and task index </td></tr>
<tr id="row_92_67_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_typed_flags.html" target="_self">shark::TypedFlags&lt; Flag &gt;</a></td><td class="desc">Flexible and extensible mechanisms for holding flags </td></tr>
<tr id="row_93_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_iterative_n_n_query.html" target="_self">shark::IterativeNNQuery&lt; DataContainer &gt;</a></td><td class="desc">Iterative nearest neighbors query </td></tr>
<tr id="row_94_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_jaakkola_heuristic.html" target="_self">shark::JaakkolaHeuristic</a></td><td class="desc">Jaakkola's heuristic and related quantities for Gaussian kernel selection </td></tr>
<tr id="row_95_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_kernel_matrix.html" target="_self">shark::KernelMatrix&lt; InputType, CacheType &gt;</a></td><td class="desc">Kernel Gram matrix </td></tr>
<tr id="row_96_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_key_value_pair.html" target="_self">shark::KeyValuePair&lt; Key, Value &gt;</a></td><td class="desc">Represents a Key-Value-Pair similar std::pair which is strictly ordered by it's key </td></tr>
<tr id="row_97_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_labeled_data_distribution.html" target="_self">shark::LabeledDataDistribution&lt; InputType, LabelType &gt;</a></td><td class="desc">A <a class="el" href="classshark_1_1_labeled_data_distribution.html" title="A LabeledDataDistribution defines a supervised learning problem.">LabeledDataDistribution</a> defines a supervised learning problem </td></tr>
<tr id="row_98_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_labeled_data_distribution.html" target="_self">shark::LabeledDataDistribution&lt; InputType, unsigned int &gt;</a></td><td class="desc"></td></tr>
<tr id="row_99_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_99_" class="arrow" onclick="toggleFolder('99_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_labeled_data_distribution.html" target="_self">shark::LabeledDataDistribution&lt; RealVector, RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_99_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_wave.html" target="_self">shark::Wave</a></td><td class="desc">Noisy sinc function: y = sin(x) / x + noise </td></tr>
<tr id="row_100_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_100_" class="arrow" onclick="toggleFolder('100_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_labeled_data_distribution.html" target="_self">shark::LabeledDataDistribution&lt; RealVector, unsigned int &gt;</a></td><td class="desc"></td></tr>
<tr id="row_100_0_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_chessboard.html" target="_self">shark::Chessboard</a></td><td class="desc">"chess board" problem for binary classification </td></tr>
<tr id="row_100_1_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_circle_in_square.html" target="_self">shark::CircleInSquare</a></td><td class="desc"></td></tr>
<tr id="row_100_2_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_diagonal_with_circle.html" target="_self">shark::DiagonalWithCircle</a></td><td class="desc"></td></tr>
<tr id="row_100_3_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_pami_toy.html" target="_self">shark::PamiToy</a></td><td class="desc"></td></tr>
<tr id="row_101_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_lib_s_v_m_selection_criterion.html" target="_self">shark::LibSVMSelectionCriterion</a></td><td class="desc">Computes the maximum gian solution </td></tr>
<tr id="row_102_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_linear_neuron.html" target="_self">shark::LinearNeuron</a></td><td class="desc">Linear activation Neuron </td></tr>
<tr id="row_103_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_linear_ranking_selection.html" target="_self">shark::LinearRankingSelection&lt; Ordering &gt;</a></td><td class="desc">Implements a fitness-proportional selection scheme for mating selection that scales the fitness values linearly before carrying out the actual selection </td></tr>
<tr id="row_104_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_logistic_neuron.html" target="_self">shark::LogisticNeuron</a></td><td class="desc">Neuron which computes the Logistic (logistic) function with range [0,1] </td></tr>
<tr id="row_105_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_l_r_u_cache.html" target="_self">shark::LRUCache&lt; T &gt;</a></td><td class="desc">Implements an LRU-Caching Strategy for arbitrary Cache-Lines </td></tr>
<tr id="row_106_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_l_r_u_cache.html" target="_self">shark::LRUCache&lt; QpFloatType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_107_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_markov_chain.html" target="_self">shark::MarkovChain&lt; Operator &gt;</a></td><td class="desc">A single Markov chain </td></tr>
<tr id="row_108_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_maximum_gain_criterion.html" target="_self">shark::MaximumGainCriterion</a></td><td class="desc">Working set selection by maximization of the dual objective gain </td></tr>
<tr id="row_109_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_maximum_gradient_criterion.html" target="_self">shark::MaximumGradientCriterion</a></td><td class="desc">Working set selection by maximization of the projected gradient </td></tr>
<tr id="row_110_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_mc_pegasos.html" target="_self">shark::McPegasos&lt; VectorType &gt;</a></td><td class="desc"><a class="el" href="classshark_1_1_pegasos.html" title="Pegasos solver for linear (binary) support vector machines.">Pegasos</a> solver for linear multi-class support vector machines </td></tr>
<tr id="row_111_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_111_" class="arrow" onclick="toggleFolder('111_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><b>detail::MklKernelBase</b></td><td class="desc"></td></tr>
<tr id="row_111_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_mkl_kernel.html" target="_self">shark::MklKernel&lt; InputType &gt;</a></td><td class="desc">Weighted sum of kernel functions </td></tr>
<tr id="row_111_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_multi_task_kernel.html" target="_self">shark::MultiTaskKernel&lt; InputTypeT &gt;</a></td><td class="desc">Special kernel function for multi-task and transfer learning </td></tr>
<tr id="row_112_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_m_n_i_s_t.html" target="_self">shark::MNIST</a></td><td class="desc">Reads in the famous <a class="el" href="classshark_1_1_m_n_i_s_t.html" title="Reads in the famous MNIST data in possibly binarized form. The MNIST database itself is not included ...">MNIST</a> data in possibly binarized form. The <a class="el" href="classshark_1_1_m_n_i_s_t.html" title="Reads in the famous MNIST data in possibly binarized form. The MNIST database itself is not included ...">MNIST</a> database itself is not included in <a class="el" href="classshark_1_1_shark.html" title="Allows for querying compile settings at runtime. Provides the current command line arguments to the r...">Shark</a>, this class just helps loading it </td></tr>
<tr id="row_113_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_modified_kernel_matrix.html" target="_self">shark::ModifiedKernelMatrix&lt; InputType, CacheType &gt;</a></td><td class="desc">Modified Kernel Gram matrix </td></tr>
<tr id="row_114_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_multi_nomial_distribution.html" target="_self">shark::MultiNomialDistribution</a></td><td class="desc">Implements a multinomial distribution </td></tr>
<tr id="row_115_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_multi_variate_normal_distribution.html" target="_self">shark::MultiVariateNormalDistribution</a></td><td class="desc">Implements a multi-variate normal distribution with zero mean </td></tr>
<tr id="row_116_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_multi_variate_normal_distribution_cholesky.html" target="_self">shark::MultiVariateNormalDistributionCholesky</a></td><td class="desc">Multivariate normal distribution with zero mean using a cholesky decomposition </td></tr>
<tr id="row_117_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_m_v_p_selection_criterion.html" target="_self">shark::MVPSelectionCriterion</a></td><td class="desc">Computes the most violating pair of the problem </td></tr>
<tr id="row_118_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_c_a_r_tree_1_1_node.html" target="_self">shark::CARTree&lt; LabelType &gt;::Node</a></td><td class="desc"></td></tr>
<tr id="row_119_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_normalizer_neuron.html" target="_self">shark::NormalizerNeuron&lt; VectorType &gt;</a></td><td class="desc">Normalizes the sum of inputs to one </td></tr>
<tr id="row_120_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_n_s_g_a3_indicator.html" target="_self">shark::NSGA3Indicator</a></td><td class="desc"></td></tr>
<tr id="row_121_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_one_point_crossover.html" target="_self">shark::OnePointCrossover</a></td><td class="desc">Implements one-point crossover </td></tr>
<tr id="row_122_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_partially_mapped_crossover.html" target="_self">shark::PartiallyMappedCrossover</a></td><td class="desc">Implements partially mapped crossover </td></tr>
<tr id="row_123_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_partly_precomputed_matrix.html" target="_self">shark::PartlyPrecomputedMatrix&lt; Matrix &gt;</a></td><td class="desc">Partly Precomputed version of a matrix for quadratic programming </td></tr>
<tr id="row_124_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_pegasos.html" target="_self">shark::Pegasos&lt; VectorType &gt;</a></td><td class="desc"><a class="el" href="classshark_1_1_pegasos.html" title="Pegasos solver for linear (binary) support vector machines.">Pegasos</a> solver for linear (binary) support vector machines </td></tr>
<tr id="row_125_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_penalizing_evaluator.html" target="_self">shark::PenalizingEvaluator</a></td><td class="desc">Penalizing evaluator for scalar objective functions </td></tr>
<tr id="row_126_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_hypervolume_contribution_approximator_1_1_point.html" target="_self">shark::HypervolumeContributionApproximator::Point&lt; VectorType &gt;</a></td><td class="desc">Models a point and associated information for book-keeping purposes </td></tr>
<tr id="row_127_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_evaluation_archive_1_1_point_result_pair_type.html" target="_self">shark::EvaluationArchive&lt; PointType, ResultT &gt;::PointResultPairType</a></td><td class="desc">Pair of point and result </td></tr>
<tr id="row_128_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_polynomial_mutator.html" target="_self">shark::PolynomialMutator</a></td><td class="desc">Polynomial mutation operator </td></tr>
<tr id="row_129_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_population_based_step_size_adaptation.html" target="_self">shark::PopulationBasedStepSizeAdaptation</a></td><td class="desc">Step size adaptation based on the success of the new population compared to the old </td></tr>
<tr id="row_130_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_precomputed_matrix.html" target="_self">shark::PrecomputedMatrix&lt; Matrix &gt;</a></td><td class="desc">Precomputed version of a matrix for quadratic programming </td></tr>
<tr id="row_131_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_qp_mc_box_decomp_1_1_prefered_selection_strategy.html" target="_self">shark::QpMcBoxDecomp&lt; Matrix &gt;::PreferedSelectionStrategy</a></td><td class="desc">Working set selection eturning th S2DO working set </td></tr>
<tr id="row_132_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_qp_mc_simplex_decomp_1_1_prefered_selection_strategy.html" target="_self">shark::QpMcSimplexDecomp&lt; Matrix &gt;::PreferedSelectionStrategy</a></td><td class="desc">Working set selection eturning th S2DO working set </td></tr>
<tr id="row_133_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_133_" class="arrow" onclick="toggleFolder('133_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><b>Problem</b></td><td class="desc"></td></tr>
<tr id="row_133_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_box_based_shrinking_strategy.html" target="_self">shark::BoxBasedShrinkingStrategy&lt; Problem &gt;</a></td><td class="desc">Takes q boxx constrained QP-type problem and implements shrinking on it </td></tr>
<tr id="row_134_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_box_linear.html" target="_self">shark::QpBoxLinear&lt; InputT &gt;</a></td><td class="desc">Quadratic program solver for box-constrained problems with linear kernel </td></tr>
<tr id="row_135_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_135_" class="arrow" onclick="toggleFolder('135_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_config.html" target="_self">shark::QpConfig</a></td><td class="desc">Super class of all support vector machine trainers </td></tr>
<tr id="row_135_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_svm_trainer.html" target="_self">shark::AbstractSvmTrainer&lt; InputType, unsigned int, KernelClassifier&lt; InputType &gt;, AbstractWeightedTrainer&lt; KernelClassifier&lt; InputType &gt; &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_135_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_svm_trainer.html" target="_self">shark::AbstractSvmTrainer&lt; InputType, RealVector, KernelExpansion&lt; InputType &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_135_2_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_svm_trainer.html" target="_self">shark::AbstractSvmTrainer&lt; InputType, unsigned int, MissingFeaturesKernelExpansion&lt; InputType &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_135_3_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_svm_trainer.html" target="_self">shark::AbstractSvmTrainer&lt; InputType, unsigned int, KernelExpansion&lt; InputType &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_135_4_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_svm_trainer.html" target="_self">shark::AbstractSvmTrainer&lt; InputType, unsigned int &gt;</a></td><td class="desc"></td></tr>
<tr id="row_135_5_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_linear_svm_trainer.html" target="_self">shark::AbstractLinearSvmTrainer&lt; InputType &gt;</a></td><td class="desc">Super class of all linear SVM trainers </td></tr>
<tr id="row_135_6_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_abstract_svm_trainer.html" target="_self">shark::AbstractSvmTrainer&lt; InputType, LabelType, Model, Trainer &gt;</a></td><td class="desc">Super class of all kernelized (non-linear) SVM trainers </td></tr>
<tr id="row_135_7_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_one_class_svm_trainer.html" target="_self">shark::OneClassSvmTrainer&lt; InputType, CacheType &gt;</a></td><td class="desc">Training of one-class SVMs </td></tr>
<tr id="row_136_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_mc_box_decomp.html" target="_self">shark::QpMcBoxDecomp&lt; Matrix &gt;</a></td><td class="desc"></td></tr>
<tr id="row_137_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_137_" class="arrow" onclick="toggleFolder('137_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_mc_linear.html" target="_self">shark::QpMcLinear&lt; InputT &gt;</a></td><td class="desc">Generic solver skeleton for linear multi-class SVM problems </td></tr>
<tr id="row_137_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_mc_linear_a_d_m.html" target="_self">shark::QpMcLinearADM&lt; InputT &gt;</a></td><td class="desc">Solver for the multi-class SVM with absolute margin and discriminative maximum loss </td></tr>
<tr id="row_137_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_mc_linear_a_t_m.html" target="_self">shark::QpMcLinearATM&lt; InputT &gt;</a></td><td class="desc">Solver for the multi-class SVM with absolute margin and total maximum loss </td></tr>
<tr id="row_137_2_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_mc_linear_a_t_s.html" target="_self">shark::QpMcLinearATS&lt; InputT &gt;</a></td><td class="desc">Solver for the multi-class SVM with absolute margin and total sum loss </td></tr>
<tr id="row_137_3_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_mc_linear_c_s.html" target="_self">shark::QpMcLinearCS&lt; InputT &gt;</a></td><td class="desc">Solver for the multi-class SVM by Crammer &amp; Singer </td></tr>
<tr id="row_137_4_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_mc_linear_l_l_w.html" target="_self">shark::QpMcLinearLLW&lt; InputT &gt;</a></td><td class="desc">Solver for the multi-class SVM by Lee, Lin &amp; Wahba </td></tr>
<tr id="row_137_5_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_mc_linear_m_m_r.html" target="_self">shark::QpMcLinearMMR&lt; InputT &gt;</a></td><td class="desc">Solver for the multi-class maximum margin regression SVM </td></tr>
<tr id="row_137_6_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_mc_linear_reinforced.html" target="_self">shark::QpMcLinearReinforced&lt; InputT &gt;</a></td><td class="desc">Solver for the "reinforced" multi-class SVM </td></tr>
<tr id="row_137_7_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_mc_linear_w_w.html" target="_self">shark::QpMcLinearWW&lt; InputT &gt;</a></td><td class="desc">Solver for the multi-class SVM by Weston &amp; Watkins </td></tr>
<tr id="row_138_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_mc_simplex_decomp.html" target="_self">shark::QpMcSimplexDecomp&lt; Matrix &gt;</a></td><td class="desc"></td></tr>
<tr id="row_139_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_qp_solution_properties.html" target="_self">shark::QpSolutionProperties</a></td><td class="desc">Properties of the solution of a quadratic program </td></tr>
<tr id="row_140_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_solver.html" target="_self">shark::QpSolver&lt; Problem, SelectionStrategy &gt;</a></td><td class="desc">Quadratic program solver </td></tr>
<tr id="row_141_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_qp_sparse_array.html" target="_self">shark::QpSparseArray&lt; QpFloatType &gt;</a></td><td class="desc">Specialized container class for multi-class SVM problems </td></tr>
<tr id="row_142_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_qp_stopping_condition.html" target="_self">shark::QpStoppingCondition</a></td><td class="desc">Stopping conditions for quadratic programming </td></tr>
<tr id="row_143_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_individual_1_1_rank_ordering.html" target="_self">shark::Individual&lt; PointType, FitnessTypeT, Chromosome &gt;::RankOrdering</a></td><td class="desc">Ordering relation by the ranks of the individuals </td></tr>
<tr id="row_144_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_real_space.html" target="_self">shark::RealSpace</a></td><td class="desc">The <a class="el" href="structshark_1_1_real_space.html" title="The RealSpace can&#39;t be enumerated. Infinite values are just too much.">RealSpace</a> can't be enumerated. Infinite values are just too much </td></tr>
<tr id="row_145_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1tags_1_1_real_space.html" target="_self">shark::tags::RealSpace</a></td><td class="desc">A Tag for EnumerationSpaces. It tells the Functions, that the space is real and can't be enumerated </td></tr>
<tr id="row_146_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_rectifier_neuron.html" target="_self">shark::RectifierNeuron</a></td><td class="desc">Rectifier Neuron f(x) = max(0,x) </td></tr>
<tr id="row_147_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_reference_vector_adaptation.html" target="_self">shark::ReferenceVectorAdaptation&lt; IndividualType &gt;</a></td><td class="desc">Reference vector adaptation for the <a class="el" href="classshark_1_1_r_v_e_a.html" title="Implements the RVEA algorithm.">RVEA</a> algorithm </td></tr>
<tr id="row_148_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_reference_vector_adaptation.html" target="_self">shark::ReferenceVectorAdaptation&lt; shark::Individual &gt;</a></td><td class="desc"></td></tr>
<tr id="row_149_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_reference_vector_guided_selection.html" target="_self">shark::ReferenceVectorGuidedSelection&lt; IndividualType &gt;</a></td><td class="desc">Implements the reference vector selection for the <a class="el" href="classshark_1_1_r_v_e_a.html" title="Implements the RVEA algorithm.">RVEA</a> algorithm </td></tr>
<tr id="row_150_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_reference_vector_guided_selection.html" target="_self">shark::ReferenceVectorGuidedSelection&lt; shark::Individual &gt;</a></td><td class="desc"></td></tr>
<tr id="row_151_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_regularized_kernel_matrix.html" target="_self">shark::RegularizedKernelMatrix&lt; InputType, CacheType &gt;</a></td><td class="desc">Kernel Gram matrix with modified diagonal </td></tr>
<tr id="row_152_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_radius_margin_quotient_1_1_result.html" target="_self">shark::RadiusMarginQuotient&lt; InputType, CacheType &gt;::Result</a></td><td class="desc"></td></tr>
<tr id="row_153_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_result_set.html" target="_self">shark::ResultSet&lt; SearchPointT, ResultT &gt;</a></td><td class="desc"></td></tr>
<tr id="row_154_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_154_" class="arrow" onclick="toggleFolder('154_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_result_set.html" target="_self">shark::ResultSet&lt; SearchPointTypeT, double &gt;</a></td><td class="desc"></td></tr>
<tr id="row_154_0_" class="odd" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_154_0_" class="arrow" onclick="toggleFolder('154_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_single_objective_result_set.html" target="_self">shark::SingleObjectiveResultSet&lt; SearchPointTypeT &gt;</a></td><td class="desc">Result set for single objective algorithm </td></tr>
<tr id="row_154_0_0_" class="odd" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_validated_single_objective_result_set.html" target="_self">shark::ValidatedSingleObjectiveResultSet&lt; SearchPointTypeT &gt;</a></td><td class="desc">Result set for validated points </td></tr>
<tr id="row_155_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1statistics_1_1_result_table.html" target="_self">shark::statistics::ResultTable&lt; Parameter &gt;</a></td><td class="desc">Stores results of a running experiment </td></tr>
<tr id="row_156_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_r_f_trainer.html" target="_self">shark::RFTrainer&lt; LabelType &gt;</a></td><td class="desc">Random Forest </td></tr>
<tr id="row_157_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_r_o_c.html" target="_self">shark::ROC</a></td><td class="desc">ROC-Curve - false negatives over false positives </td></tr>
<tr id="row_158_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_roulette_wheel_selection.html" target="_self">shark::RouletteWheelSelection</a></td><td class="desc">Fitness-proportional selection operator </td></tr>
<tr id="row_159_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_qp_sparse_array_1_1_row.html" target="_self">shark::QpSparseArray&lt; QpFloatType &gt;::Row</a></td><td class="desc"><a class="el" href="classshark_1_1_data.html" title="Data container.">Data</a> structure describing a row of the sparse array </td></tr>
<tr id="row_160_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_abstract_objective_function_1_1_second_order_derivative.html" target="_self">shark::AbstractObjectiveFunction&lt; PointType, ResultT &gt;::SecondOrderDerivative</a></td><td class="desc"></td></tr>
<tr id="row_161_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_shape.html" target="_self">shark::Shape</a></td><td class="desc">Represents the <a class="el" href="classshark_1_1_shape.html" title="Represents the Shape of an input or output.">Shape</a> of an input or output </td></tr>
<tr id="row_162_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_shark.html" target="_self">shark::Shark</a></td><td class="desc">Allows for querying compile settings at runtime. Provides the current command line arguments to the rest of the library </td></tr>
<tr id="row_163_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_163_" class="arrow" onclick="toggleFolder('163_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><b>SHARK_ITERATOR_FACADE</b></td><td class="desc"></td></tr>
<tr id="row_163_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_indexed_iterator.html" target="_self">shark::IndexedIterator&lt; Iterator &gt;</a></td><td class="desc">Creates an Indexed Iterator, an Iterator which also carries index information using <a class="el" href="classshark_1_1_indexed_iterator.html#addc567fdd49114d13a2084d2c197df33">index()</a> </td></tr>
<tr id="row_163_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_indexing_iterator.html" target="_self">shark::IndexingIterator&lt; Container &gt;</a></td><td class="desc"></td></tr>
<tr id="row_163_2_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_proxy_iterator.html" target="_self">shark::ProxyIterator&lt; Sequence, ValueType, Reference &gt;</a></td><td class="desc">Creates an iterator which reinterpretes an object as a range </td></tr>
<tr id="row_164_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_shifter.html" target="_self">shark::Shifter</a></td><td class="desc"><a class="el" href="classshark_1_1_shifter.html" title="Shifter problem.">Shifter</a> problem </td></tr>
<tr id="row_165_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_165_" class="arrow" onclick="toggleFolder('165_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><b>shark::detail::SimpleBatch&lt; WeightedDataBatch&lt; detail::element_to_batch&lt; DataType &gt;::type, detail::element_to_batch&lt; WeightType &gt;::type &gt; &gt;</b></td><td class="desc"></td></tr>
<tr id="row_165_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_batch_3_01_weighted_data_pair_3_01_data_type_00_01_weight_type_01_4_01_4.html" target="_self">shark::Batch&lt; WeightedDataPair&lt; DataType, WeightType &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_166_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_simulated_binary_crossover.html" target="_self">shark::SimulatedBinaryCrossover&lt; PointType &gt;</a></td><td class="desc">Simulated binary crossover operator </td></tr>
<tr id="row_167_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_simulated_binary_crossover.html" target="_self">shark::SimulatedBinaryCrossover&lt; RealVector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_168_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_simulated_binary_crossover.html" target="_self">shark::SimulatedBinaryCrossover&lt; SearchPointType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_169_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_single_pole.html" target="_self">shark::SinglePole</a></td><td class="desc"></td></tr>
<tr id="row_170_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_softmax_neuron.html" target="_self">shark::SoftmaxNeuron&lt; VectorType &gt;</a></td><td class="desc">Computes the softmax activation function </td></tr>
<tr id="row_171_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_171_" class="arrow" onclick="toggleFolder('171_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_state.html" target="_self">shark::State</a></td><td class="desc">Represents the <a class="el" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a> of an Object </td></tr>
<tr id="row_171_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_empty_state.html" target="_self">shark::EmptyState</a></td><td class="desc">Default <a class="el" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a> of an Object which does not need a <a class="el" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a> </td></tr>
<tr id="row_171_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_normalizer_neuron_1_1_state.html" target="_self">shark::NormalizerNeuron&lt; VectorType &gt;::State</a></td><td class="desc"></td></tr>
<tr id="row_172_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1statistics_1_1_statistics.html" target="_self">shark::statistics::Statistics&lt; Parameter &gt;</a></td><td class="desc">Generates <a class="el" href="structshark_1_1statistics_1_1_statistics.html" title="Generates Statistics over the results of an experiment.">Statistics</a> over the results of an experiment </td></tr>
<tr id="row_173_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_173_" class="arrow" onclick="toggleFolder('173_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><b>shark::detail::SubrangeKernelBase&lt; InputType &gt;</b></td><td class="desc"></td></tr>
<tr id="row_173_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_subrange_kernel.html" target="_self">shark::SubrangeKernel&lt; InputType, InnerKernel &gt;</a></td><td class="desc">Weighted sum of kernel functions </td></tr>
<tr id="row_174_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_174_" class="arrow" onclick="toggleFolder('174_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_svm_problem.html" target="_self">shark::SvmProblem&lt; Problem &gt;</a></td><td class="desc"></td></tr>
<tr id="row_174_0_" class="odd" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_174_0_" class="arrow" onclick="toggleFolder('174_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_box_based_shrinking_strategy.html" target="_self">shark::BoxBasedShrinkingStrategy&lt; SvmProblem&lt; Problem &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_174_0_0_" class="odd" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_svm_shrinking_problem.html" target="_self">shark::SvmShrinkingProblem&lt; Problem &gt;</a></td><td class="desc"></td></tr>
<tr id="row_175_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_tanh_neuron.html" target="_self">shark::TanhNeuron</a></td><td class="desc">Neuron which computes the hyperbolic tangenst with range [-1,1] </td></tr>
<tr id="row_176_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_weighted_sum_kernel_1_1t_base.html" target="_self">shark::WeightedSumKernel&lt; InputType &gt;::tBase</a></td><td class="desc">Structure describing a single m_base kernel </td></tr>
<tr id="row_177_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_tempered_markov_chain.html" target="_self">shark::TemperedMarkovChain&lt; Operator &gt;</a></td><td class="desc"></td></tr>
<tr id="row_178_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_timer.html" target="_self">shark::Timer</a></td><td class="desc"><a class="el" href="classshark_1_1_timer.html" title="Timer abstraction with microsecond resolution.">Timer</a> abstraction with microsecond resolution </td></tr>
<tr id="row_179_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_tournament_selection.html" target="_self">shark::TournamentSelection&lt; Predicate &gt;</a></td><td class="desc">Tournament selection operator </td></tr>
<tr id="row_180_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structboost_1_1serialization_1_1tracking__level_3_01shark_1_1_typed_flags_3_01_t_01_4_01_4.html" target="_self">boost::serialization::tracking_level&lt; shark::TypedFlags&lt; T &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_181_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structboost_1_1serialization_1_1tracking__level_3_01std_1_1vector_3_01_t_01_4_01_4.html" target="_self">boost::serialization::tracking_level&lt; std::vector&lt; T &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_182_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_transformed_data.html" target="_self">shark::TransformedData&lt; Functor, T &gt;</a></td><td class="desc"></td></tr>
<tr id="row_183_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_tree_construction.html" target="_self">shark::TreeConstruction</a></td><td class="desc">Stopping criteria for tree construction </td></tr>
<tr id="row_184_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_two_point_step_size_adaptation.html" target="_self">shark::TwoPointStepSizeAdaptation</a></td><td class="desc">Step size adaptation based on the success of the new population compared to the old </td></tr>
<tr id="row_185_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_two_state_space.html" target="_self">shark::TwoStateSpace&lt; State1, State2 &gt;</a></td><td class="desc">The <a class="el" href="structshark_1_1_two_state_space.html" title="The TwoStateSpace is a discrete Space with only two values, for example {0,1} or {-1,...">TwoStateSpace</a> is a discrete Space with only two values, for example {0,1} or {-1,1} </td></tr>
<tr id="row_186_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_186_" class="arrow" onclick="toggleFolder('186_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><b>std::conditional::type</b></td><td class="desc"></td></tr>
<tr id="row_186_0_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_batch.html" target="_self">shark::Batch&lt; detail::MarkovChainSample&lt; HiddenSample, VisibleSample &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_187_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_187_" class="arrow" onclick="toggleFolder('187_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><b>std::conditional::type</b></td><td class="desc"></td></tr>
<tr id="row_187_0_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_187_0_" class="arrow" onclick="toggleFolder('187_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_batch.html" target="_self">shark::Batch&lt; detail::MatrixRowReference&lt; M &gt;::Vector &gt;</a></td><td class="desc"></td></tr>
<tr id="row_187_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_batch_3_01detail_1_1_matrix_row_reference_3_01_m_01_4_01_4.html" target="_self">shark::Batch&lt; detail::MatrixRowReference&lt; M &gt; &gt;</a></td><td class="desc"></td></tr>
<tr id="row_188_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_188_" class="arrow" onclick="toggleFolder('188_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><b>std::conditional::type</b></td><td class="desc"></td></tr>
<tr id="row_188_0_" class="odd" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_batch.html" target="_self">shark::Batch&lt; T &gt;</a></td><td class="desc">Class which helps using different batch types </td></tr>
<tr id="row_189_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classshark_1_1_uniform_crossover.html" target="_self">shark::UniformCrossover</a></td><td class="desc">Uniform crossover of arbitrary individuals </td></tr>
<tr id="row_190_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_uniform_ranking_selection.html" target="_self">shark::UniformRankingSelection</a></td><td class="desc">Selects individuals from the range of individual and offspring individuals </td></tr>
<tr id="row_191_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_qp_mc_box_decomp_1_1_variable.html" target="_self">shark::QpMcBoxDecomp&lt; Matrix &gt;::Variable</a></td><td class="desc"><a class="el" href="classshark_1_1_data.html" title="Data container.">Data</a> structure describing one m_variables of the problem </td></tr>
<tr id="row_192_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_qp_mc_simplex_decomp_1_1_variable.html" target="_self">shark::QpMcSimplexDecomp&lt; Matrix &gt;::Variable</a></td><td class="desc"><a class="el" href="classshark_1_1_data.html" title="Data container.">Data</a> structure describing one variable of the problem </td></tr>
<tr id="row_193_" class="odd"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_193_" class="arrow" onclick="toggleFolder('193_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><b>shark::detail::VectorBatch&lt; blas::matrix&lt; T, blas::row_major, Device &gt; &gt;</b></td><td class="desc"></td></tr>
<tr id="row_193_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_batch_3_01blas_1_1vector_3_01_t_00_01_device_01_4_01_4.html" target="_self">shark::Batch&lt; blas::vector&lt; T, Device &gt; &gt;</a></td><td class="desc">Specialization for vectors which should be matrices in batch mode! </td></tr>
<tr id="row_194_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_shark_1_1_version.html" target="_self">shark::Shark::Version&lt; major, minor, patch &gt;</a></td><td class="desc">Models a version according to the major.minor.patch versioning scheme </td></tr>
<tr id="row_195_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_weighted_data_batch.html" target="_self">shark::WeightedDataBatch&lt; DataBatchType, WeightBatchType &gt;</a></td><td class="desc"></td></tr>
<tr id="row_196_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_weighted_data_pair.html" target="_self">shark::WeightedDataPair&lt; DataType, WeightType &gt;</a></td><td class="desc">Input-Label pair of data </td></tr>
<tr id="row_197_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structshark_1_1_w_s2_maximum_gradient_criterion.html" target="_self">shark::WS2MaximumGradientCriterion</a></td><td class="desc">Working set selection by maximization of the projected gradient </td></tr>
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